in Ecology

Tree species richness relates to long-term forest photosynthesis increase

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Abstract

The widespread increase in vegetation productivity plays an important role in enhancing ecosystem carbon uptake. While it is well established that biodiversity increases ecosystem productivity, its influence on long-term changes in photosynthesis remains unclear. Here we integrate a high-resolution map of tree species richness with satellite-derived photosynthesis proxies during 2001–2020 to show that high richness not only enhances current levels of photosynthesis but also correlates with a greater increase in photosynthesis over time. This pattern is largely driven by an amplified CO2 fertilization effect (CFE) in species-rich forests. The ability of diverse forests to mitigate water and nutrient limitations probably contributes to the CFE enhancement and photosynthesis rise. Projections suggest that biodiversity losses by 2050 could reduce photosynthesis trends by 3–17%, representing a cumulative forest photosynthesis loss of 4.4–35.7 PgC. These findings underscore the critical need to integrate biodiversity conservation into climate mitigation strategies to safeguard the terrestrial carbon sink.

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Fig. 1: Effects of species richness on long-term variations in photosynthesis.
Fig. 2: Effects of species richness on photosynthesis in response to elevated CO2 concentration.
Fig. 3: Relationships of species richness with limitations of water availability and nutrients.
Fig. 4: Cumulative effect of S on long-term photosynthesis from 2015 to 2050.

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Data availability

The global tree species richness map is available via Figshare at https://doi.org/10.6084/m9.figshare.20055488 (ref. 96). Ground-sourced forest plot data are available from the GFBI (https://www.gfbinitiative.org/data) and via Figshare for the BIEN dataset at https://doi.org/10.6084/m9.figshare.7436951 (ref. 97) and the compiled dataset (https://doi.org/10.6084/m9.figshare.7461509)98. Satellite-derived photosynthesis and vegetation products are available from CSIF (https://osf.io/8xqy6), GOSIF (http://data.globalecology.unh.edu/data/GOSIF_v2), LCSIF (https://doi.org/10.5281/zenodo.7916851 and https://doi.org/10.5281/zenodo.7916879)99,100, GOME2 (https://www.earthdata.nasa.gov/data/projects/sif-esdr and https://avdc.gsfc.nasa.gov/pub/data/satellite/MetOp/), FluxSat GPP (https://avdc.gsfc.nasa.gov/pub/tmp/FluxSat_GPP/), CEDAR GPP (https://doi.org/10.5281/zenodo.10712978)101, GLOMAP LAI (https://doi.org/10.5281/zenodo.4700264)102, MODIS LAI (http://globalchange.bnu.edu.cn/research/laiv061), GIMMS LAI4g (https://doi.org/10.5281/zenodo.8281930)103, FPAR (https://doi.org/10.5281/zenodo.8076540)104 and MODIS MCD43A4 (https://doi.org/10.5067/MODIS/MCD43A4.061)105. CRU TS v.4.07 climate data are from https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/. ERA5-Land data are from https://cds.climate.copernicus.eu/datasets. NOAA atmospheric CO2 observations are from https://gml.noaa.gov/ccgg/trends/. SoilGrids 2.0 data are available at https://data.isric.org/geonetwork/srv/chi/catalog.search#/metadata/d95b6733-5a29-475e-ab83-478dcb8c0c20. Altitude data are from https://global-hydrodynamics.github.io/MERIT_DEM/. Aboveground biomass data are from UNEP-WCMC (https://data-gis.unep-wcmc.org/portal/home/item.html?id=8a8d4e24683a46e6b039aea78c8af20f)80. Forest age data are available at https://doi.org/10.17871/ForestAgeBGI.2021 (ref. 106). ESA CCI land-cover data are available at http://maps.elie.ucl.ac.be/CCI/viewer/download.php. Hansen forest cover and loss data are from https://earthenginepartners.appspot.com/science-2013-global-forest (ref. 70). ALOS PALSAR forest/non-forest data are from https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_PALSAR_YEARLY_FNF4#description. Intact Forest Landscapes data are from https://intactforests.org/data.ifl.html. Global foliar nitrogen and phosphorus datasets are available via Zenodo at https://doi.org/10.5281/zenodo.7825970 (ref. 107) and TRY-related products (https://isp.uv.es/code/try.html)93. CMIP6 model outputs are available from https://esgf-node.llnl.gov/projects/cmip6. Projected changes in species richness are available from https://www.sciencebase.gov/catalog/item/65fc456dd34e64ff1548d31b (ref. 60). A full list of datasets and sources is provided in Supplementary Table 8.

Code availability

All analyses were conducted in MATLAB R2024a and R v.4.1.3. The main code is available via Zenodo at https://doi.org/10.5281/zenodo.20447683 (ref. 108). The MATLAB package M_Map (https://www.eoas.ubc.ca/~rich/map.html) was used to create the maps in this study109.

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Acknowledgements

We acknowledge J. Liang from Purdue University for providing the global tree species richness map and offering constructive and insightful comments. We thank B. Dechant and H. Vallicrosa for their generous provision of the global foliar elemental composition maps. We also appreciate the valuable feedback from Y. Wang at CSIRO Environment and Y. Luo at Cornell University. We are deeply indebted to the data providers and the managers of the GFBI forest inventory database. Our sincere thanks also extend to all data providers for their continuous efforts and for sharing their data. The tree, leaf and sun icons used in Extended Data Fig. 3 were adapted from the design of Freepik (www.freepik.com).

Funding

This research was supported by the National Science Foundation of China (42125105) to Y.Z., the Spanish Government grant PID2022-140808NB-I00 funded by the Spanish MICIU/AEI/10.13039/501100011033 and FEDER European Union and the European Union grant CONCERTO (HORIZON-CL5-2024-D1-01). M.F.-M. was supported by the European Research Council project ERC-StG-2022-101076740 STOIKOS and a Ramón y Cajal fellowship (RYC2021-031511-I) funded by the Spanish Ministry of Science and Innovation, the NextGenerationEU programme of the European Union, the Spanish plan of recovery, transformation and resilience and the Spanish Agency of Research.

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Authors and Affiliations

  1. International Institute for Earth System Sciences, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing University, Nanjing, China

    Ruochen Cao & Yongguang Zhang

  2. State Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University, Nanjing, China

    Ruochen Cao & Yongguang Zhang

  3. CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain

    Ruochen Cao, Jordi Sardans, Marcos Fernández-Martínez, Adrià Descals & Josep Peñuelas

  4. Frontiers Science Center for Critical Earth Material Cycling, Nanjing University, Nanjing, China

    Yongguang Zhang

  5. Huangshan National Park Ecosystem Field Scientific Observation and Research Station of the Ministry of Education, Nanjing, China

    Yongguang Zhang

  6. European Commission, Joint Research Centre (JRC), Ispra, Italy

    Alessandro Cescatti

  7. Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France

    Philippe Ciais

  8. CREAF, Cerdanyola del Vallès, Barcelona, Spain

    Jordi Sardans, Marcos Fernández-Martínez, Adrià Descals & Josep Peñuelas

  9. The Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Chaoyang Wu

  10. Institute for Future Initiatives, University of Tokyo (UTokyo), Tokyo, Japan

    Constantin M. Zohner

  11. BRANCH Institute, Zug, Switzerland

    Constantin M. Zohner

Authors

  1. Ruochen Cao
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  2. Yongguang Zhang
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  3. Alessandro Cescatti
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  4. Philippe Ciais
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  5. Jordi Sardans
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  6. Chaoyang Wu
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  7. Constantin M. Zohner
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  8. Marcos Fernández-Martínez
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  9. Adrià Descals
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Contributions

Y.Z. conceived the study. R.C., Y.Z. and A.C. developed the methodology. Y.Z. and R.C. conducted the investigation. R.C. and Y.Z. performed the visualization. Y.Z. acquired funding and managed the project. Y.Z. and J.P. supervised the study. R.C., Y.Z. and J.P. wrote the original draft. Y.Z., R.C., J.P., A.C., P.C., J.S., C.W., C.M.Z., M.F.-M. and A.D. reviewed and edited the paper.

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Yongguang Zhang.

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Extended data

Extended Data Fig. 1 Consistent effects of species richness on long-term photosynthesis trends derived from non-linear sequential regression models.

Species richness (S) was first modelled as a function of multiple environmental covariates using two alternative non-linear formulations that differ in their treatment of predictor non-linearity (see Supplementary Text 3, Eqs. 11–12). The residual component of S was then used to estimate its independent effect on the temporal trend of forest photosynthesis (n = 17,860 grid cells). Bar plots show standardized regression coefficients of S derived from the non-linear models. P values were evaluated using two-sided t-tests; no adjustment was made for multiple comparisons.

Extended Data Fig. 2 Comparison of partial correlations between photosynthesis trends and leaf area index (LAI), tree cover, and species richness.

LAI values were derived from three satellite-based products. Partial correlations were computed while controlling for air temperature, soil moisture, their temporal trends, soil organic carbon content, and elevation. P values were evaluated using two-sided t-tests; no adjustment was made for multiple comparisons.

Extended Data Fig. 3 Conceptual diagram illustrating the structural and physiological pathways through which species richness influences the temporal trend of forest photosynthesis.

Species richness may influence photosynthesis trends through a structural pathway mediated by trends in leaf area index (LAI) and a physiological pathway mediated by trends in photosynthesis per unit leaf area (Pleaf). Pleaf was calculated as photosynthesis divided by LAI. Credit: tree, leaf and sun icons, Freepik.com.

Extended Data Fig. 4 Structural equation model (SEM) analysis of the pathways through which species richness (S) influences photosynthesis trends.

a, Conceptual diagram illustrating direct and indirect effects of S on photosynthesis trends, mediated by trends in leaf area index (LAI) and photosynthesis per unit leaf area (Pleaf). Temp, air temperature; SM, soil moisture; SOC, soil organic carbon content. b, Standardized indirect effects of S on photosynthesis trends via structural (that is, LAI) and physiological (that is, Pleaf) pathways. The asterisks indicate significance (***, P < 0.001) from two-sided t-tests; no adjustment was made for multiple comparisons. The bars represent the mean coefficients across SEMs based on three sets of LAI products (MODIS, GLOMAP, and GIMMS), with the error bars denoting s.e.m. The colored symbols show the corresponding estimates from individual LAI products. Each SEM was fitted using n = 16,948 grid cells after excluding grid cells with missing LAI values.

Extended Data Fig. 5 Results of partial correlations between species richness and the temporal trend of light use efficiency derived from different SIF and GPP datasets during 2001–2020.

Partial correlations were computed while controlling for air temperature, soil moisture, their temporal trends, background light use efficiency, soil organic carbon content, and elevation. P values were evaluated using two-sided t-tests; no adjustment was made for multiple comparisons. See Supplementary Text 2 for details.

Extended Data Fig. 6 Coefficients of partial correlation analysis at different spatial resolutions.

Results were calculated using 0.05° and 0.1° spatial resolution. P values were evaluated using two-sided t-tests; no adjustment was made for multiple comparisons.

Extended Data Fig. 7 Coefficients of partial correlations between species richness and the increasing trend of photosynthesis.

The influences of climatic, soil, biotic, and topographic factors were accounted for in the partial correlation analysis. Relative trend of photosynthesis was calculated by the ratio of the photosynthesis trend to the mean background photosynthesis (%). P values were evaluated using two-sided t-tests; no adjustment was made for multiple comparisons.

Extended Data Fig. 8 Results of effects of species richness (S) on long-term trends in photosynthesis using the structural equation model.

a, Suggested causal pathways of direct and indirect effects of S, environmental factors, forest age, and background photosynthesis on photosynthesis trends. This model was designed specifically to disentangle the interplay between S and environmental conditions in shaping photosynthesis trends, given that higher biodiversity often occurs in regions with favourable conditions for vegetation growth. This approach also highlights the role of S as a key bridge linking environmental factors to photosynthesis changes. b, The standardized coefficients of the direct, indirect and total effect of S on the temporal trend of photosynthesis. c. Total effects of each predictor on the photosynthesis trend. BP, background photosynthesis; Temp, air temperature; SM, soil moisture; SOC, soil organic carbon content; Age, forest age.

Extended Data Fig. 9 Associations of S with the temporal stability of forest photosynthesis.

a, The relationship between S and the temporal stability of forest photosynthesis across global forests after accounting for background air temperature and SM. The red line represents the fitted linear regression relationship (n = 25,118). b, Spatial pattern of the S effect on the stability of forest photosynthesis using a spatially moving-window method. The dots are spaced at 3.5° for both latitude and longitude. Dot size represents the significance level. The inset in the lower left corner represents the frequency of statistically significant (P < 0.05) positive (‘(+)’) or negative (‘(-)’) areas in the map. c, Zonal average of effect size along latitude gradients. The shaded areas represent one standard deviation. P values were evaluated using two-sided t-tests with no adjustment for multiple comparisons. Basemap in b generated with M_Map (https://www.eoas.ubc.ca/~rich/map.html).

Extended Data Fig. 10 Associations of S with the sensitivity of forest photosynthesis to the aridity index.

a, Spatial pattern of the sensitivity of forest photosynthesis to the aridity index (AI, the ratio of precipitation to potential evapotranspiration) over the period 2001–2020. b, The relationship between S and the sensitivity of forest photosynthesis to AI across global forests after accounting for background air temperature and SM. The red line represents the fitted linear regression relationship. The regression analysis included only forest grid cells with positive sensitivity values (n = 14,321). c, Spatial pattern of the S effect on the sensitivity of forest photosynthesis to AI using a spatially moving-window method. The dots are spaced at 3.5° for both latitude and longitude. Dot size represents the significance level. The inset in the lower left corner represents the frequency of statistically significant (P < 0.05) positive (‘(+)’) or negative (‘(-)’) areas in the map. P values were evaluated using two-sided t-tests with no adjustment for multiple comparisons. Basemaps in a,c generated with M_Map (https://www.eoas.ubc.ca/~rich/map.html).

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Supplementary Text 1–9, Figs. 1–25 and Tables 1–8.

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Cao, R., Zhang, Y., Cescatti, A. et al. Tree species richness relates to long-term forest photosynthesis increase.
Nat. Clim. Chang. (2026). https://doi.org/10.1038/s41558-026-02698-7

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